Tea App Checker — agentic threat model

Not certain from the listing — It is unclear if an LLM is utilized to generate reports or redact screenshots. If a foundation model is used, threats include prompt injection designed to bypass redaction filters or extract underlying raw transcript data.

Not certain from the listing — The agent processes sensitive user-submitted identifying details and scraped dating app data. While it claims automatic deletion after 7 days, threats include data exfiltration of cached search histories or database compromise during the retention window.

Not certain from the listing — No orchestration framework is specified. If an agentic framework is used to automate the lookup, insecure tool integration could allow an attacker to manipulate input parameters to perform unauthorized bulk queries.

Not certain from the listing — Aside from mentioning SSL encryption for Stripe payments, the hosting and sandboxing infrastructure is not described. Threats include server-side request forgery (SSRF) if the agent dynamically fetches external profile URLs.

Not certain from the listing — There is no mention of logging, drift detection, or guardrails. A lack of rate-limiting or anomaly detection could allow malicious actors to abuse the service for automated stalking or bulk reconnaissance.

The agent implements clear security controls including SSL encryption via Stripe and a strict 7-day data retention policy. However, processing third-party dating app data without the target's explicit consent presents significant privacy and regulatory compliance risks (e.g., GDPR/CCPA).

Not certain from the listing — The agent operates as a standalone vertical service. There are no indications of multi-agent coordination or ecosystem integrations that could introduce cascading trust boundaries.